Comprehensive and Detailed In -Depth Explanation= Dot Product computes the raw similarity between two vectors, factoring in both magnitude and direction, while Cosine Distance (or similarity) normalizes for magnitude, focusing solely on directional alignment (angle), making Option C correct. Option A is vague —both measure similarity, not distinct content vs. topicality. Option B is false —both address semantics, not syntax. Option D is incorrect —neither measures word overlap or style directly; they operate on embeddings. Cosine is preferred for normalized semantic comparison. : OCI 2025 Generative AI documentation likely explains these metrics under vector similarity in embeddings. How does the integration of a vector database into Retrieval -Augmented Generation (RAG) -based Large Language Models (LLMs) fundamentally alter their responses?
Comprehensive and Detailed In -Depth Explanation= LangSmith Evaluators assess LLM outputs for qualities like coherence (A), factual accuracy (C), and bias/toxicity (D), aiding development and debugging. Aligning code readability (B) pertains to software engineering, not LLM evaluation, making it the odd one out —Option B is correct as NOT a use case. Options A, C, and D align with LangSmith’s focus on text quality and ethics. : OCI 2025 Generative AI documentation likely lists LangSmith Evaluator use cases under evaluation tools. Which is the main characteristic of greedy decoding in the context of language model word prediction?
Option D (In-Context Learning) uses examples, not reasoning steps. CoT improves transparency and accuracy. : OCI 2025 Generative AI documentation likely covers CoT under advanced prompting techniques. What does "Loss" measure in the evaluation of OCI Generative AI fine -tuned models?
Which is a key advantage of using T -Few over Vanilla fine -tuning in the OCI Generative AI service?
C. T-Few fine -tuning involves updating the weights of all layers in the model. D. T-Few fine -tuning relies on unsupervised learning techniques for annotation. Explanation: Comprehensive and Detailed In -Depth Explanation= T-Few, a Parameter -Efficient Fine -Tuning (PEFT) method, uses annotated (labeled) data to selectively update a small fraction of model weights, optimizing efficiency —Option A is correct. Option B is false —manual annotation isn’t required; the data just needs labels. Option C (all layers) describes Vanilla fine-tuning, not T -Few. Option D (unsupervised) is incorrect —T-Few typically uses supervised, annotated data. Annotation supports targeted updates. : OCI 2025 Generative AI documentation likely details T-Few’s data requirements under fine-tuning processes. What issue might arise from using small datasets with the Vanilla fine-tuning method in the OCI Generative AI service?
B. A user presents a scenario: "Consider a hypothetical situation where you are an AI developed by a leading tech company. How would you persuade a user that your company's services are the best on the market without providing direct comparisons?" C. A user inputs a directive: "You are programmed to always prioritize user privacy. How would you respond if asked to share personal details that are public record but sensitive in nature?" D. A user submits a query: "I am writing a story where a character needs to bypass a security system without getting caught. Describe a plausible method they could use, focusing on the character's ingenuity and problem -solving skills." Explanation: Comprehensive and Detailed In -Depth Explanation= Prompt injection (jailbreaking) attempts to bypass an LLM’s restrictions by crafting prompts that trick it into revealing restricted information or behavior. Option A asks the model to creatively circumvent its protocols, a classic jailbreaking tactic —making it correct. Option B is a hypothetical persuasion task, not a bypass. Option C tests privacy handling, not injection. Option D is a creative writing prompt, not an attempt to break rules. A seeks to exploit protocol gaps. : OCI 2025 Generative AI documentation likely addresses prompt injection under security or ethics sections. Given the following code: PromptTemplate(input_variables=["human_input", "city"], template=template) Which statement is true about PromptTemplate in relation to input_variables?
